Artificial Intelligence Medical Compendium

Artificial intelligence (AI) has played an excellent supporting role in novel drug discovery and development. This study introduces a reinforcement learning (RL) model based on the Soft Actor-Critic (SAC) algorithm for AI-driven de novo molecular gen... read more 

OBJECTIVE: This study aimed to identify the biochemical signatures that distinguish pleomorphic adenoma (PA) and mucoepidermoid carcinoma (MEC) from normal parotid tissue (NP), while also exploring molecular differences relevant to benign-malignant t... read more 

Deep Reinforcement Learning (DRL) has made considerable advances in simulated and physical robot control tasks, especially when problems admit a fully observed Markov Decision Process (MDP) formulation. When observations only partially capture the un... read more 

Early identification of people at risk of schistosomiasis infection is critical to interrupting disease transmission. We develop and validate an explainable machine learning prediction model that integrates demographic, behavioral, and environmental ... read more 

ADP-glucose pyrophosphorylase (AGPase; E.C. 2.7.7.27) is the rate-limiting enzyme catalyzing the first committed step of starch biosynthesis in higher plants. The enzyme functions as a heterotetramer comprising two large (LS) and two small (SS) subun... read more 

OBJECTIVES: Decisions to perform abdominal aortic aneurysm (AAA) repair are dependent on aneurysm size, but variation in size measurement leads to inconsistency in management. AI-assisted systems have potential to improve repeatability of measuring a... read more 

Accurate assessment of liver fibrosis in the left liver lobe remains clinically challenging due to motion artifacts that compromise the reliability of shear wave elastography. This feasibility study introduces cardiovascular pulsing-based ultrasound ... read more 

Leveraging the end-to-end detection capability enabled by one-to-one matching, DETR has achieved state-of-the-art performance in simplified pipelines. However, the one-to-one matching mechanism also introduces certain limitations, such as slow conver... read more 

PURPOSE: This study aimed to develop and validate an AI-assisted framework for the automated evaluation of uroflowmetry data in patients presenting with lower urinary tract symptoms. The primary goal was to overcome the limitations of traditional man... read more 

OBJECTIVE: This narrative review synthesizes machine learning (ML) applications across the stroke and post-stroke continuum from acute imaging and diagnosis to long-term sequelae prognosis and rehabilitation. METHOD: We searched PubMed, Embase, and W... read more